Method and system for extracting an actual surgical duration from a total operating room (or) time of a surgical procedure

ABSTRACT

Embodiments described herein provide various examples of a system for extracting an actual procedure duration composed of actual surgical tool-tissue interactions from an overall procedure duration of a surgical procedure on a patient. In one aspect, the system is configured to obtain the actual procedure duration by: obtaining an overall procedure duration of the surgical procedure; receiving a set of operating room (OR) data from a set of OR data sources collected during the surgical procedure, wherein the set of OR data includes an endoscope video captured during the surgical procedure; analyzing the set of OR data to detect a set of non-surgical events during the surgical procedure that do not involve surgical tool-tissue interactions; extracting a set of durations corresponding to the set of non-surgical events; and determining the actual procedure duration by subtracting the set of extracted durations from the overall procedure duration.

PRIORITY CLAIM AND RELATED PATENT APPLICATIONS

This patent application is a continuation of, and hereby claims thebenefit of priority under 35 U.S.C. § 120 to co-pending U.S. patentapplication Ser. No. 16/221,045, filed on 14 Dec. 2018 (Attorney DocketNo. 210231P1067US), entitled, “Method and System for Extracting anActual Surgical Duration from a Total Operating Room (OR) Time of aSurgical Procedure,” by inventors Jagadish Venkataraman and Pablo G.Kilroy. The above-listed application is hereby incorporated by referenceas a part of this patent document.

TECHNICAL FIELD

The present disclosure generally relates to buildingmachine-learning-based surgical procedure analysis tools and, morespecifically, to systems, devices and techniques for extracting anactual surgical duration from a total operating room (OR) time of asurgical procedure based on multiple sources of data collected duringthe surgical procedure.

BACKGROUND

Operating room (OR) costs are among one of the highest medical andhealthcare-related costs. With skyrocketing healthcare expenditures,OR-costs management aimed at reducing OR costs and increasing ORefficiency has become an increasingly important research subject. ORcosts are often measured based on a per-minute cost structure. Forexample, one 2005 study shows that the OR costs range from $22 to $133per minute with an average cost of $62 per minute. In this per-minutecost structure, the OR costs of a given surgical procedure are directlyproportional to the duration/length of the surgical procedure. Hence, ORtime-management and scheduling plays a central role in overall OR-costsmanagement. Clearly, OR time-management and scheduling are highlydependent on the duration of a particular type of surgical procedure(also referred to as “procedure time”). For example, OR time for aparticular type of surgical procedure can be allocated based on anaverage duration of that surgical procedure. While predicting theaverage duration of a given surgical procedure can be a highly complextask, it is possible to collect a large amount ofsurgical-procedure-related data and estimate the average duration forthe given surgical procedure based on data analyses.

In some conventional surgical procedure analyses, the duration of asurgical procedure is simply measured as a time period between themoment when the patient arrives at the OR (i.e., wheeled-in time) andthe moment when the patient leaves the OR (i.e., wheeled-out time).Based on these analyses, a first surgical procedure of a given typeperformed by a first surgeon may have a total OR time of 50 minutesbetween the wheeled-in time and the wheeled-out time, while a secondsurgical procedure of the same type performed by a second surgeon mayhave a total OR time of one hour and five minutes. When comparing thesetwo surgical procedures, one may conclude that the first surgeon is farmore efficient than the second surgeon. However, evaluating surgeonskill/efficiency or an overall OR efficiency based on comparing thetotal OR times can be extremely flawed. This is because a total OR timeof a surgical procedure is typically composed of varioussegments/events, and the amount of time the surgeon is actuallyoperating on the patient with surgical tools is only one part of thetotal OR time. In the above-described example, the two surgeons can havesubstantially the same efficiency, while the time difference in thetotal OR times can be caused by the inefficiency of the support team ofthe second surgeon, e.g., when performing tool exchanges. In this case,the times that the surgeon waits for the right tools to be brought incontribute to the total OR time, but are not part of the actual surgicalduration.

Some advanced ORs have a multitude of sensors and cameras installed formonitoring OR efficiency. However, these sensors and cameras areprimarily used for monitoring patient and surgical staff movementsinside the OR and identifying OR efficiency based on the observedmovements. Unfortunately, there are no known data analysis tools thatperform root-cause analysis on OR videos and sensor data to determinethe root causes of OR inefficiency that lead to long total OR times.

SUMMARY

In this patent disclosure, various examples of a surgical procedureanalysis system for breaking down a total operating room (OR) time of asurgical procedure into a series of identifiable events, categorizingthe events, and determining the duration of each of the identifiableevents are disclosed. In particular, these identifiable events caninclude a set of “non-surgical events” that do not involve interactionsbetween surgical tools and the patient's tissues (i.e., when the surgeonis not performing actual surgical tasks on the patient). For example,the set of non-surgical events can include but are not limited to:“patient preparations” before and after the actual surgical procedure;“out-of-body events” when the endoscope is taken outside of thepatient's body for various reasons; “tool exchange events” when oneactively used surgical tool is being replaced with another surgical tooland the associated wait times; and “surgical timeout events” when thesurgeon pauses for various reasons and is not performing actual surgicaltasks on the patient. Moreover, the identifiable events also include aset of actual surgical segments separated by the set of non-surgicalevents and involving interactions between surgical tools and thepatient's tissues (i.e., when the surgeon is performing actual surgicaltasks on the patient). Consequently, the actual surgical procedureduration (or “actual procedure duration”), i.e., the actual amount oftime the surgeon interacts with the patient's tissues using surgicaltools, can be extracted from the total OR time by subtracting/excludingfrom the total OR time those non-surgical events that do not involveinteractions between surgical tools and the patient's tissues.

In various embodiments, the disclosed surgical procedure analysis systemidentifies various non-surgical events from the total OR time byanalyzing one or multiple of the following data sources collected duringthe total OR time: one or more endoscope videos; recorded OR videos fromwall/ceiling cameras; pressure sensors at the doorway of the OR;pressure sensors on the surgical platform; pressure sensors on the tipsor jaws of the surgical tools; and/or recorded OR audios. In variousembodiments, the disclosed surgical procedure analysis system includesmachine-learning modules that can be applied to one or more of the abovedata sources to identify both non-surgical events and surgical events.In particular, the disclosed surgical procedure analysis system caninclude machine-learning modules for identifying out-of-body eventsbased on endoscope videos; machine-learning modules for identifying toolexchange events based on endoscope videos; machine-learning modules foridentifying surgical timeout events based on endoscope videos; andmachine-learning modules for identifying actual surgical segments basedon endoscope videos.

In some embodiments, the various predictions made based on endoscopevideos can be combined with other data sources to improve the confidencelevels of the predictions. For example, the actual surgical segmentpredictions made based on endoscope videos can be combined with datafrom pressure sensors on the surgical tool tips/jaws to improve theconfidence levels of the predictions. As another example, theout-of-body event predictions based on endoscope videos can be combinedwith videos from the wall/ceiling cameras to improve the confidencelevels of the predictions.

In various embodiments, the disclosed surgical procedure analysis systemcan also include machine-learning modules for tracking personnelmovements inside the OR based on videos from wall/ceiling cameras;machine-learning modules for identifying certain out-of-body eventsbased on videos from wall/ceiling cameras; and machine-learning modulesfor identifying certain surgical timeout events based on videos fromwall/ceiling cameras (e.g., a wait time for a collaborating surgeon toarrive). In some embodiments, the disclosed surgical procedure analysissystem can also include machine-learning modules for determiningwheeled-in and wheeled-out times based on pressure sensors at thedoorway of the OR; machine-learning modules for determining certaintimeout events (e.g., repositioning of the surgical tools) based onpressure sensors on the surgical platform; and machine-learning modulesfor determining certain timeout events (e.g., delays caused by OR roomchats/discussions) based on audios recorded in the OR.

In one aspect, a process extracting an actual procedure durationcomposed of actual surgical tool-tissue interactions from an overallprocedure duration of a surgical procedure on a patient is disclosed.This process can begin by obtaining the overall procedure duration ofthe surgical procedure performed by a surgeon on the patient. Theprocess then receives a set of operating room (OR) data from a set of ORdata sources collected during the surgical procedure, wherein the set ofOR data includes an endoscope video captured during the surgicalprocedure. Next, the process analyzes the set of OR data to detect a setof non-surgical events during the surgical procedure that do not involvesurgical tool-tissue interactions. For example, analyzing the set of ORdata includes performing a machine-learning-based analysis on theendoscope video. The process then extracts a set of durationscorresponding to the set of non-surgical events. Finally, the processdetermines the actual procedure duration by subtracting the set ofdurations corresponding to the set of non-surgical events from theoverall procedure duration.

In some embodiments, the set of OR data further includes one or more ofthe following: a set of sensor data collected inside the OR during thesurgical procedure; a set of audio files recorded inside the OR duringthe surgical procedure; and one or more videos captured by one or morewall and/or ceiling cameras inside the OR during the surgical procedure.

In some embodiments, the set of sensor data further includes one or moreof the following: pressure sensor data collected from surgical toolsinvolved in the surgical procedure; pressure sensor data collected froma surgical platform inside the OR; and pressure sensor data collectedfrom a doorway of the OR.

In some embodiments, the process analyzes the set of OR data to identifya surgical timeout event, wherein a surgical timeout event occurs withina surgical phase of the surgical procedure when the surgeon pausesperforming surgical tasks on the patient for a certain time period.

In some embodiments, the process identifies the surgical timeout eventby performing a machine-learning-based analysis on the endoscope videoto determine that the movement of a surgical tool in the endoscope videohas stopped for more than a predetermined time period.

In some embodiments, the process extracts the duration of the identifiedsurgical timeout event by: extracting an initial time of the identifiedsurgical timeout event when the movement of the surgical tool isdetermined to have stopped based on the machine-learning-based analysis;and extracting an end time of the identified surgical timeout event whenthe movement the surgical tool is determined to have resumed based onthe machine-learning-based analysis.

In some embodiments, the process collaborates the extracted initial timeand end time of the identified surgical timeout event with pressuresensor data collected from a pressure sensor located at the tip of thesurgical tool.

In some embodiments, the process collaborates the extracted initial timeand end time with the pressure sensor data by: collaborating theextracted initial time with a first time when the pressure sensor datadecreases to substantially zero; and collaborating the extracted endtime with a second time when the pressure sensor data increases fromsubstantially zero to a significant value.

In some embodiments, the surgical timeout event occurs for one of thefollowing set of reasons: (1) when the surgeon stops interacting withthe patient and starts a discussion with another surgeon, the surgicalsupport team, or a resident surgeon; (2) when the surgeon pauses to makea decision on how to proceed with the surgical procedure based on aon-screen event or a surgical complication; and (3) when the surgeonpauses to wait for a collaborating surgeon to come into the OR.

In some embodiments, the process analyzes the set of OR data to furtheridentify a set of out-of-body (OOB) events, wherein an OOB event beginswhen an endoscope used during the surgical procedure is taken out of thepatient's body for one of a set of reasons and ends when the endoscopeis being inserted back into the patient's body.

In some embodiments, the process identifies an OOB event by performing amachine-learning-based analysis on the endoscope video to (1) identifythe beginning of the OOB event based on a first sequence of video imagesin the endoscope video, and (2) identify the end of the OOB event basedon a second sequence of video images in the endoscope video.

In some embodiments, a given OOB event occurs because of one of afollowing set of reasons: cleaning the endoscope lens when an endoscopicview is partially or entirely blocked; changing the endoscope lens fromone scope size to another scope size; and switching the surgicalprocedure from a robotic surgical system to a laparoscopic surgicalsystem.

In some embodiments, analyzing the set of OR data to detect a set ofnon-surgical events during the surgical procedure further includesidentifying a pre-surgery patient preparation time prior to the surgicalprocedure and identifying a post-surgery patient assistant time afterthe completion of the surgical procedure.

In some embodiments, the process obtains the overall procedure durationof the surgical procedure by determining a time when the patient isbeing wheeled into the OR and a time when the patient is being wheeledout of the OR.

In another aspect, a process extracting an actual procedure durationcomposed of actual surgical tool-tissue interactions from an overallprocedure duration of a surgical procedure on a patient is disclosed.This process can begin by obtaining the overall procedure duration ofthe surgical procedure performed by a surgeon on the patient. Theprocess then receives a set of operating room (OR) data from a set of ORdata sources collected during the surgical procedure, wherein the set ofOR data includes an endoscope video captured during the surgicalprocedure. Next, the process analyzes the set of OR data to detect a setof non-surgical events during the surgical procedure that do not involvesurgical tool-tissue interactions. For example, analyzing the set of ORdata includes collaborating the endoscope video with the set of sensordata. The process then extracts a set of durations corresponding to theset of non-surgical events. Finally, the process determines the actualprocedure duration by subtracting the set of durations corresponding tothe set of non-surgical events from the overall procedure duration

In some embodiments, the set of sensor data further includes one or moreof the following: pressure sensor data collected from surgical toolsinvolved in the surgical procedure; pressure sensor data collected froma surgical platform inside the OR; and pressure sensor data collectedfrom a doorway of the OR.

In some embodiments, the set of OR data further includes a set of audiofiles recorded inside the OR during the surgical procedure and one ormore videos captured by one or more wall and/or ceiling cameras insidethe OR during the surgical procedure.

BRIEF DESCRIPTION OF THE DRAWINGS

The structure and operation of the present disclosure will be understoodfrom a review of the following detailed description and the accompanyingdrawings in which like reference numerals refer to like parts and inwhich:

FIG. 1 shows a diagram illustrating an exemplary operating room (OR)environment with a robotic surgical system in accordance with someembodiments described herein.

FIG. 2 illustrates a timeline of an exemplary full surgical procedureindicating various surgical and non-surgical events that make up theduration of the full surgical procedure in accordance with someembodiments described herein.

FIG. 3 presents a flowchart illustrating an exemplary process forperforming automatic HIPAA-compliant video editing in accordance withsome embodiments described herein.

FIG. 4 presents a block diagram illustrating the interrelationshipsbetween various types of events that constitute the total OR time of asurgical procedure and the set of data sources available inside the ORduring the surgical procedure in accordance with some embodimentsdescribed herein.

FIG. 5 shows a block diagram of an exemplary surgical procedure analysissystem in accordance with some embodiments described herein.

FIG. 6 conceptually illustrates a computer system with which someembodiments of the subject technology can be implemented.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology may bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a thorough understandingof the subject technology. However, the subject technology is notlimited to the specific details set forth herein and may be practicedwithout these specific details. In some instances, structures andcomponents are shown in block diagram form in order to avoid obscuringthe concepts of the subject technology.

FIG. 1 shows a diagram illustrating an exemplary operating room (OR)environment 110 with a robotic surgical system 100 in accordance withsome embodiments described herein. As shown in FIG. 1, robotic surgicalsystem 100 comprises a surgeon console 120, a control tower 130, and oneor more surgical robotic arms 112 located at a robotic surgical platform116 (e.g., a table or a bed, etc.), where surgical tools with endeffectors are attached to the distal ends of the robotic arms 112 forexecuting a surgical procedure. The robotic arms 112 are shown as atable-mounted system, but in other configurations, the robotic arms maybe mounted in a cart, ceiling or sidewall, or other suitable supportsurface. Robotic surgical system 100 can include any currently existingor future-developed robot-assisted surgical systems for performingrobot-assisted surgeries.

Generally, a user/operator 140, such as a surgeon or other operator, mayuse the user console 120 to remotely manipulate the robotic arms 112and/or surgical instruments (e.g., tele-operation). User console 120 maybe located in the same operating room as robotic surgical system 100, asshown in FIG. 1. In other environments, user console 120 may be locatedin an adjacent or nearby room, or tele-operated from a remote locationin a different building, city, or country. User console 120 may comprisea seat 132, foot-operated controls 134, one or more handheld userinterface devices (UIDs) 136, and at least one user display 138configured to display, for example, a view of the surgical site inside apatient. As shown in the exemplary user console 120, a surgeon locatedin the seat 132 and viewing the user display 138 may manipulate thefoot-operated controls 134 and/or UIDs 136 to remotely control therobotic arms 112 and/or surgical instruments mounted to the distal endsof the arms.

In some variations, a user may also operate robotic surgical system 100in an “over the bed” (OTB) mode, in which the user is at the patient'sside and simultaneously manipulating a robotically driven tool/endeffector attached thereto (e.g., with a handheld user interface device(UID) 136 held in one hand) and a manual laparoscopic tool. For example,the user's left hand may be manipulating a handheld UID 136 to control arobotic surgical component, while the user's right hand may bemanipulating a manual laparoscopic tool. Thus, in these variations, theuser may perform both robotic-assisted (minimally invasive surgery) MISand manual laparoscopic surgery on a patient.

During an exemplary procedure or surgery, the patient is prepped anddraped in a sterile fashion to receive anesthesia. Initial access to thesurgical site may be performed manually with robotic surgical system 100in a stowed or withdrawn configuration to facilitate access to thesurgical site. Once the access is completed, initial positioning and/orpreparation of the robotic system may be performed. During theprocedure, a surgeon in the user console 120 may utilize thefoot-operated controls 134 and/or UIDs 136 to manipulate varioussurgical tools/end effectors and/or imaging systems to perform thesurgery. Manual assistance may also be provided at the procedure tableby sterile-gowned personnel, who may perform tasks including but notlimited to, retracting tissues or performing manual repositioning ortool exchange involving one or more robotic arms 112. Non-sterilepersonnel may also be present to assist the surgeon at the user console120. When the procedure or surgery is completed, robotic surgical system100 and/or user console 120 may be configured or set in a state tofacilitate one or more post-operative procedures, including but notlimited to, robotic surgical system 100 cleaning and/or sterilisation,and/or healthcare record entry or printout, whether electronic or hardcopy, such as via the user console 120.

In some aspects, the communication between robotic surgical platform 116and user console 120 may be through control tower 130, which maytranslate user commands from the user console 120 to robotic controlcommands and transmit the robotic control commands to robotic surgicalplatform 116. Control tower 130 may also transmit status and feedbackfrom robotic surgical platform 116 back to user console 120. Theconnections between robotic surgical platform 116, user console 120 andcontrol tower 130 can be via wired and/or wireless connections, and canbe proprietary and/or performed using any of a variety of datacommunication protocols. Any wired connections may be optionally builtinto the floor and/or walls or ceiling of the operating room. Roboticsurgical system 100 can provide video output to one or more displays,including displays within the operating room as well as remote displaysaccessible via the Internet or other networks. The video output or feedmay also be encrypted to ensure privacy and all or portions of the videooutput may be saved to a server or electronic healthcare record system.

In addition to robotic surgical system 100, OR environment 110 alsoincludes a wall camera 142 and multiple ceiling cameras 144. During anOR procedure, these cameras can capture staff movements, surgeonmovements, activities over surgical platform 116, and movements at adoorway 146 of the OR. Hence, the videos captured by wall camera 142 andceiling cameras 144 during the OR procedure can provide direct visualinformation related to surgical staff performance, patient preparationsbefore and after a surgical procedure, surgical tool exchanges,endoscope lens exchanges and cleaning, surgeons taking timeouts, andwaiting for a collaborating surgeon, among others. OR environment 110can also include voice recording devices such as a microphone 150.During an OR procedure, microphone 150 can record audio feeds within ORenvironment 110 including discussions between surgeons and other peopleinside or outside OR environment 110.

Various sensors can be installed within OR environment 110 to provideadditional information related to surgical procedures taking placeinside OR environment 110. For example, OR environment 110 can includepressure sensors 148 installed at doorway 146 that can be used toestimate the times when the patients are being wheeled into and wheeledout of OR environment 110. OR environment 110 also includes pressuresensors 152 installed on surgical platform 116 that can detect eventswhen patients are being transferred onto and removed from surgicalplatform 116, as well as events when patients are being repositioned onsurgical platform 116 during surgical procedures for various purposes,such as creating a new port in the patient's body to allow for bettertool access. Although not visible in FIG. 1, surgical tools/endeffectors attached to robotic arms 112 can include pressure sensors onthe tool tips or jaws of the tools that can detect events when the toolsare actually interacting with tissues, and events when the tools areinside the patient's body but idle for various reasons.

In addition to the various data sources within OR environment 110described above, full surgical procedure videos captured by endoscopecameras remain one of the most important data sources for analyzingsurgical procedures, such as estimating the actual duration of thesurgical procedure (or “actual procedure duration” hereinafter). Actualprocedure duration can be considered as the amount of time when thesurgeon is actually operating on the patient by manipulating thesurgical tools. Hence, the actual procedure duration includes the timesthat involve tool-tissue interactions, i.e., when the surgical toolsactually interact with the tissues. However, the total recorded OR time(or “the total OR time”) for a given surgical procedure is typicallymeasured from the moment the patient is wheeled into the OR to themoment the patient is wheeled out of the OR. We also refer to the entireOR process corresponding to this total recorded OR time as “the overallsurgical procedure” or “the full surgical procedure” hereinafter.

Note that the total OR time can include various time periods duringwhich no tool-tissue interactions take place. For example, these timeperiods can be associated with the surgeon waiting for a tool to bebrought in, taking a timeout to discuss with his surgical team, takingthe endoscope out of the patient's body to be cleaned, and so on. Assuch, the actual procedure duration is generally not continuous butsegmented by a set of non-tool-tissue-interaction events (or“non-surgical events” for simplicity) during which time the surgeon isnot applying surgical tools on the tissues and, hence, no tool-tissueinteraction is taking place. By definition, the total OR time is the sumof the combined time of all of these non-surgical events and theabove-described actual procedure duration. Consequently, if thesenon-surgical events can be identified within the total OR time and theirdurations extracted, the actual procedure duration can be determined byexcluding the combined time of these non-surgical events from the totalOR time.

Some embodiments described herein aim to detect these non-surgicalevents by combining various available data sources within the ORincluding procedure videos from endoscope cameras, OR videos from thewall cameras and/or ceiling cameras, and various sensor data from the ORincluding: data from pressure sensors on the doors of the OR, data frompressure sensors on the surgical platforms, and data from pressuresensors on the tips of the surgical tools, among others. In variousembodiments, the above-described data sources can be analyzed/minedusing various machine-learning-based techniques, conventionalcomputer-vision techniques such as image processing, or a combination ofmachine-learning and computer-vision techniques to identify thesenon-surgical events and extract the associated event durations. We nowdescribe different types of the non-surgical events in more detailbelow.

Patient Preparation Before the Surgical Procedure

This is a time period when the surgical staff prepares the patient forsurgery, after the patient has been wheeled into the OR. The time periodcan include transferring the patient from the wheeled bed onto thesurgical platform, positioning the patient on the surgical platform forconvenient surgical tool access, arranging patient's clothing forsurgical tool access, preparing the patient's skin for incision, andpositioning the surgical tools over the patient's body, among otherthings. Note that during this preparation time no endoscope video isavailable because the endoscope camera has not been introduced into thepatient's body. We also refer to this time period as the “pre-surgerypreparation time” or “the first patient preparation time” hereinafter.

Tool Exchange Times/Events

A tool exchange event is a time period when one actively used surgicaltool is being replaced with another surgical tool. More specifically,during a tool exchange time/event, the actively used surgical tool isbeing taken out of the patient's body and the next surgical tool isbeing brought into the patient's body. Note that, in addition to thetime needed to remove one tool and bring in another tool, the toolexchange time can also include time that the surgeon waits for the newtool. This wait time can be affected by the readiness of the surgicalsupport team in the OR. It is appreciated that, a surgical support teamthat understands the surgeon's technique and tool needs can have theright tool supplies available at the right times for tool exchange tohappen smoothly. In contrast, if the surgical support team changes dueto absence of some personnel, it is not uncommon for surgeons to waitaround for the right tool to show up, which can be retrieved from theinventory outside of the OR during the surgical procedure. Other factorsthat can contribute to the wait time during tool exchange include: timeto sterilize the new tool (if the tool is not sterilized); time to opena new tool; and time to warm up a new tool (a cold tool cannot beinserted into a patient's body because the patient can go into shock).

In some embodiments, tool exchange times can be detected based on theendoscope videos, because during a tool exchange event the endoscopegenerally remains inside the patient's body and continues to record. Forexample, the beginning of a tool exchange event can be identified whenit is detected that an actively used tool disappears from the videoimages; and the end of a tool exchange event can be identified when itis detected that a new tool appears in the video images. Becausedetecting a tool exchange event involves detecting and recognizingmultiple surgical tools, a machine-learning based analysis can beapplied to the endoscope videos, e.g., by identifying the beginning ofthe tool exchange event based on a first sequence of video images andidentifying the end of the tool exchange event based on a secondsequence of video images.

In other embodiments, tool exchange times can be detected based on ORvideos from wall and ceiling cameras or based on the pressure sensordata from the tips or the jaws of the two surgical tools involved in thetool exchange event. However, the tool exchange times can be detectedand durations inferred mainly based on the endoscope videos; thepredictions made based on endoscope videos can be combined with ORvideos from wall and ceiling cameras and/or pressure sensor data fromthe tips or the jaws of the surgical tools to improve the confidencelevels of the predictions.

Out-of-Body (OOB) Times/Events

An OOB time/event is generally defined as a time period when theendoscope is taken out of the patient's body for one of various reasonsduring the surgical procedure while the endoscope camera continues torecord, or right before and/or right after the surgical procedure whilethe endoscope camera is recording, so that the endoscope video isavailable for analysis. An initial OOB time/event can exist at thebeginning of a surgical procedure if the endoscope camera is turned onprior to being inserted into the patient's body; and a final OOBtime/event can exist at the end of a surgical procedure if the endoscopecamera remains turned on for a period of time after the completion ofthe surgical procedure when the endoscope camera has been taken out ofthe patient's body.

During the actual surgical procedure, an OOB event can take place for anumber of reasons. For example, an OOB event will occur if the endoscopelens has to be cleaned. Note that a number of surgical events can causethe endoscopic view to be partially or entirely blocked. These surgicalevents can include, but are not limited to: (a) endoscope lens iscovered with blood and visibility is partially or completely lost (e.g.,due to a bleeding complication); (b) fogging of the endoscope lens dueto condensation, e.g., as a result of the temperature difference betweenthe lens and the patient's body; and (c) endoscope lens is covered withcautery-generated tissue particles, which stick to the lens andeventually block the endoscopic view. In each of the above scenarios,the endoscope camera needs to be taken out of the body so that theendoscope lens can be cleaned to restore visibility or warmed up forcondensation removal. After cleaning and/or other necessary treatment,the endoscope camera often needs to be re-calibrated, includingperforming white-balancing before it can be put back into the patient'sbody, which takes additional time to complete. Note that thislens-cleaning type of OOB event can take a few minutes to complete.

As another example of a different type of OOB event, sometimes during asurgical procedure, the endoscope lens needs to be changed from onescope size to another scope size for different anatomy/fields of view(FOV). For example, a surgical procedure may first use a smaller scopesize (i.e., 5 mm)/larger FOV endoscope lens to locate adifficult-to-find anatomy, and then change to a larger scope size (i.e.,10 mm)/smaller FOV endoscope lens to perform the actual operation on thespecific anatomy. In such events, the endoscope camera needs to be takenout of the patient's body so that the scope can be changed. Afterchanging the scope, the endoscope camera often needs to bere-calibrated, including performing white-balancing before it can beinserted back into the patient's body, which takes additional time tocomplete.

As yet another example of another type of OOB event, some roboticsurgical procedures today are not 100% robotic, but with a majority(e.g., 90%) of a given procedure performed with a robotic system and asmall portion (e.g., 10%) of the given procedure still performed with alaparoscopic system. In such a hybrid surgical procedure, there is atransition or downtime during the overall procedure when the roboticsystem is disengaged (including removing the endoscope camera) andlaparoscopic tools are engaged (including introducing the laparoscopiccamera). Depending on the efficiency and skills of the surgical supportteam, the transition requires a certain amount of time to move the robotaway (including disengaging the robot arms), engage the laparoscopictools, and wait for the laparoscopic surgeon to arrive at the OR. Notethat the beginning of this transition time can be identified as themoment that the endoscope video stops, while the end of the transitiontime can be identified as the moment that the laparoscopic camera imagesbegin to show. Moreover, this OOB event can be easily identifiable andtherefore distinguishable from other OOB events without system changes,because the view of the laparoscopic images is typically quite differentfrom the view of the endoscope images. For example, the endoscope viewis usually quite zoomed in so that the anatomy often occupies the fullscreen, whereas the laparoscopic view is usually circular surrounded bya black border on the screen.

Surgical Time Out Events

Surgical timeout events, or simply “surgical timeouts,” can includevarious times/events taking place during the surgical procedure when thesurgeon pauses for various reasons and is not performing actual surgicaltasks on the patient. While there can be different types of surgicaltimeout events for different reasons, each of the surgical timeoutevents can be identified based on the fact that the endoscope imagesbecome “static,” i.e., show a lack of meaningful movement. Inparticular, if there are one or more surgical tools within the field ofview, the tools would have substantially stopped moving. Note that forcertain time periods during which the associated endoscope video imagesdo not contain any surgical tools, these time periods can also beconsidered as surgical timeouts. However, if such a time period is partof the above-described tool exchange event (e.g., when the surgeon iswaiting for the new tool to arrive), the time period can be identifiedas part of the tool exchange event instead of a separate surgicaltimeout event.

As mentioned above, surgical timeouts can be caused by differentreasons. Typical surgical timeouts can be caused by, but are not limitedto, the following reasons. For example, some surgical timeouts arecaused by necessary OR discussions. More specifically, a surgicaltimeout can occur during the actual surgical procedure when the surgeonstops interacting with the tissue and starts a discussion with anothersurgeon, the surgical support team, or other residents. Thesediscussions can include discussions for making collective decisions onhow to proceed with the procedure under the current situation, such as acomplication. These discussions can also include times when the surgeonis teaching the residents who are observing the procedure.

Surgical timeouts can also occur when the surgeons make decisions (ontheir own or in consultation with the surgical support team) based onon-screen events or complications. For example, there can be timeperiods during a given procedure when a surgeon has to assess theon-screen events such as comprehending the anatomy, and/or discussinghow to deal with complications. During these times, the surgeon is onlymaking decisions, not actually operating on the patient.

On some rare occasions, surgical timeouts can occur when the surgeonbecomes lost in the on-screen events such as a complex anatomy and doesnot know how to proceed. For example, such events can happen when juniorsurgeons encounter situations they have not experienced before. Suchsituations can arise either due to taking a wrong step in the procedureor due to an unusual anatomy. To resolve such situations, the surgeonmay have to be on a call with a senior surgeon, resort to tele-operationwith an expert surgeon, or even look up on the Internet for videos ofsimilar procedures. In any of these scenarios, the surgeon will have topause to have the situation resolved without actually operating on thepatient.

Another type of surgical timeout is caused by waiting for acollaborating surgeon to come into the OR. In complex surgicalprocedures such as an esophagectomy, there can be multiple surgeonscollaborating across different phases of the procedure. In such cases,the appropriate surgeons will need to be paged at the appropriate times.However, even if the next appropriate surgeon is notified at the timewhen the current phase of the procedure is being completed, it may stilltake some time for the next surgeon to come to the OR, which canpotentially cause delays.

Patient Assistant after the Surgical Procedure

This is the final time period in the total OR time after the last stepof the actual surgical procedure has been completed. During this timeperiod the surgical support team performs necessary steps to completethe overall surgical procedure, such as closing up the incisions in thepatient, cleaning up the patient's body (e.g., removing an IV line),transferring the patient from the surgical platform to a wheeled bed,and finally wheeling the patient out of the OR, among other things. Notethat during this patient assistant time, no endoscope video is availablebecause the endoscope camera has been turned off. We also refer to thistime period as the “post-surgery preparation time” or “the secondpatient preparation time” hereinafter.

In various embodiments, the above-described data sources can beanalyzed/mined using various machine-learning-based techniques,conventional computer-vision techniques such as image processing, or acombination of machine-learning and computer-vision techniques toidentify these non-surgical events and extract the associated eventdurations. We now describe different types of the non-surgical events inmore detail below.

Some embodiments described in this disclosure aim to combine variousavailable data sources within the OR and analyze these data sourcesindividually or in combination to facilitate detecting theabove-described non-surgical events within an overall surgical procedureand determining the associated durations of the these events. In variousembodiments, the available data sources can be analyzed/mined usingvarious machine-learning-based techniques, conventional computer-visiontechniques such as image processing, or a combination ofmachine-learning and computer-vision techniques to identify thesenon-surgical events and extract the associated event durations. Bycombining data from the multiple data sources in the OR, and applyingcomputer-vision and machine-learning techniques to analyze these data,the disclosed technology can reconstruct the actual procedure durationby separating the detected events from the actual procedure duration.Alternatively, the actual procedure duration within the total OR timecan be directly determined by analyzing the same data sources, and indoing so, detecting all of the tool-tissue interaction events andextracting the associated durations of the detected tool-tissueinteraction events.

FIG. 2 illustrates a timeline of an exemplary full surgical procedure200 indicating various surgical and non-surgical events that make up theduration of the full surgical procedure in accordance with someembodiments described herein.

As can be seen in FIG. 2, full surgical procedure 200 starts at themoment the patient is wheeled into the OR, indicated as a time stampt_(wi), and ends at the moment the patient is wheeled out of the OR,indicated as a timestamp t_(wo). As mentioned above, t_(wi) and t_(wo)can be determined based on data from sensors installed in or near the ORentrance. For example, t_(wi) and t_(wo) can be determined with one ormore pressure sensors installed on the floor near the OR entrance bydetecting the combined weight of the wheeled bed and the patient on thebed. Alternatively, t_(wi) and t_(wo) can be determined by analyzingvideos from the wall camera and/or ceiling cameras that capture themoments of t_(wi) and t_(wo) at the OR entrance. In some embodiments,t_(wi) and t_(wo) can be determined by combining the pressure sensordata at the doorway and the videos from the wall and/or ceiling camerasto increase the confidence level of the estimated t_(wi) and t_(wo).

Still looking at FIG. 2, note that immediately after t_(wi) is the first(i.e., pre-surgery) preparation time T_(prep1). This is the time periodwhen the surgical staff prepares the patient for surgery, such astransferring the patient from the wheeled bed onto the surgicalplatform, positioning the patient on the surgical platform forconvenient surgical tool access, arranging the patient's clothing forsurgical tool access, preparing the patient's skin for incision, andpositioning the surgical tools over the patient's body, among otherthings. Note that during preparation time T_(prep1) no endoscope feed isavailable, because the endoscope camera has not been introduced into thepatient's body. In some embodiments, the end of preparation timeT_(prep1) can be marked as the moment when the first incision (e.g., forthe camera port) is made, because this represents the beginning ofapplying the surgical tools on the patient's body. In these embodiments,the end of preparation time T_(prep1) and the beginning of the endoscopevideo can have a very short intervening gap. In the embodiment shown inFIG. 2, however, preparation time T_(prep1) ends at the moment when theendoscope video begins, which is designated as t_(endo0). Time pointt_(endo0) can also be determined by analyzing the videos from walland/or ceiling cameras that capture the moment when the first incisionon the patient's body is made. Hence, the first preparation timeT_(prep1) can be easily determined based on the extracted values oft_(wi) and t_(endo0). In some embodiments, the first preparation timeT_(prep1) can be determined by combining the endoscope video and thewall and/or ceiling camera videos.

In some embodiments, if the estimated first preparation time T_(prep1)is significantly longer than a normal pre-surgery preparation time, thewall camera and/or ceiling camera videos can be used to identify thecause(s) of the inefficiency, e.g., based on how the actual preparationprogressed and which person or persons were responsible for the delay(s)during the patient preparation.

Note that the endoscope video recording from the endoscope camera canbegin before or after the endoscope camera has been inserted intopatient's body. Hence, t_(endo0), which represents the beginning of theendoscope video, can occur prior to or after the endoscope camerainsertion. For example, if the endoscope camera is turned on before theinsertion, t_(endo0) will occur before the insertion. The exemplarysurgical procedure 200 illustrates a scenario when t_(endo0) occursbefore the insertion when the endoscope camera remains outside of thepatient's body. As a result, the time segment immediately following andright before the t_(endo0) insertion represents an OOB event, designatedas T_(OOB1). It can be appreciated that T_(OOB1) can be determinedsimply by analyzing the beginning portion of the endoscope video. Asdescribed in more detail below, the full surgical procedure 200 caninclude multiple OOB events. Note that during these OOB events notool-tissue interactions take place within the patient's body. Hence,the overall duration of these multiple OOB events can contribute to asignificant amount of non-surgical time and, hence, OR inefficiency.

As shown in FIG. 2, the first OOB event T_(OOB1) ends at time t_(endo1),which represents the moment when the endoscope camera is inserted intothe camera port in the patient's body and anatomy images inside thepatient's body begin to show. The end of T_(OOB1) can also represent thebeginning of the actual surgical procedure when the surgical tools areinserted and begin interacting with the tissues. Note that somevariations to the exemplary surgical procedure 200 do not have T_(OOB1)if the endoscope camera is turned on during or after the insertion. Inthese cases, t_(endo0) and t_(endo1) are substantially the same timerepresenting the beginning of the endoscope video.

Still referring to FIG. 2, note that following t_(endo1), i.e., thebeginning of the endoscopic video, surgical procedure 200 comprises asequence of phases designated as P₁, P₂, and P₃. These phases areseparated by a sequence of OOB events designated as T_(OOB2), T_(OOB3),and T_(OOB4). More specifically, each of the phases P₁, P₂, and P₃ isdefined as a time period when the endoscope video is available while theendoscope camera remains inside the patient's body, while each of theOOB events T_(OOB2), T_(OOB3), and T_(OOB4) is defined as a time periodwhen the endoscope is taken out of the patient's body for variousreasons during and immediately after the actual surgical procedure. Forexample, one of the OOB events T_(OOB2) can correspond to an event whenthe endoscope is taken out of the body so that the endoscope lens can becleaned, re-calibrated (e.g., with white balancing), and thenre-inserted into the patient's body. Clearly, during these OOB events,the surgeon performing surgical procedure 200 has to wait for theendoscope and the actual surgical procedure is paused. Consequently, todetermine the actual procedure duration, the combined duration of all ofthese OOB events should be determined and excluded from the overallsurgical procedure duration between t_(wi) and t_(wo).

Note that while the exemplary surgical procedure 200 includes four OOBevents, other surgical procedures can include a fewer or greater numberof OOB events and corresponding time periods. Moreover, the last OOBtime period T_(OOB4) takes place toward the end of the surgicalprocedure 200, which is identified as the time period between atimestamp t_(endo4) corresponding to the moment the endoscope videoimages from inside of the patient's body end as the endoscope camera isbeing taken out of the patient's body, and a timestamp t_(endo5)corresponding to the moment the endoscope camera is turned off markingthe end of the endoscope video. Similar to the first OOB periodT_(OOB1), T_(OOB4) can also be determined simply by analyzing theendoscope video to identify the video image(s) corresponding tot_(endo4). Note that another surgical procedure may not have acorresponding time period like T_(OOB4) if the endoscope camera isturned off prior to or during the removal of the endoscope camera fromthe patient's body after the completion of the surgical procedure. Insuch scenarios, the end of the last procedural phase, such as P₃corresponds to the end of the endoscope video.

Note that during each of the phases P₁, P₂, and P₃, a number of othernon-surgical events can occur that do not belong to the actual procedureduration but contribute to the overall surgical procedure 200. Asmentioned above, these events can include various surgical timeouts,which can include but are not limited to the following types of events:(1) the surgeon is discussing how to proceed with the surgical supportteam or other residents; (2) the surgeon is teaching the residents; (3)the surgeon is waiting for a collaborating surgeon to arrive; and (4)the surgeon is making a decision based on on-screen events orcomplications. These additional non-surgical events can also includetool exchange events. On rare occasions, these non-surgical events canalso include scenarios when the surgeon is lost in the on-screen anatomyand does not know how to proceed.

As a specific example, phase P₁ within the exemplary surgical procedure200 can include a surgical timeout event TO₁ when the surgeon isdiscussing how to proceed with the surgical support team or residents inthe OR. This event TO₁ corresponds to a time period designated asT_(TO1). As discussed above, while surgical timeouts can take a numberof types or forms, they are typically identifiable in the endoscopevideo when the surgical tool stops moving. Because event TO₁ happenswithin phase P₁ when the endoscope video is available, event TO₁ can bedetected based on a lack of surgical tool movement. For example, when itis determined that tool movement in the endoscope video hassubstantially stopped for more than a predetermined time period, theinitial time when the movement is determined to have stopped can berecorded as the beginning of the TO₁. When the tool movement is detectedagain for the given tool, the moment when the movement is determined tohave resumed can be recorded as the end of event TO₁, and thecorresponding duration of T_(TO1) can then be extracted.

Note that it can be difficult to determine the exact cause of surgicaltimeout TO₁ solely based on the endoscope video images. For example, thelack of tool movement in the endoscope video can also be associated withother types of timeout events, such as waiting for a collaboratingsurgeon to arrive. In some embodiments, the exact nature of the detectedtimeout event TO₁ can be further predicted or verified based on therecorded OR audio signals during the same time period as timeout eventTO₁. Furthermore, these visual and audio data sources can also be usedcollaboratively with wall and/or ceiling camera videos to determine theexact nature of the timeout TO₁.

As another example of a surgical timeout event, phase P₃ in surgicalprocedure 200 is shown to include a second surgical timeout event TO₂when the surgeon is teaching the residents who are observing surgicalprocedure 200. Because event TO₂ happens within phase P₃ when theendoscope video is available, the event TO₂ can also be detected basedon the lack of surgical tool movement in the endoscope video images, andthe corresponding duration T_(TO2) can be extracted between the momentwhen the tool is determined to have stopped moving and the moment whenthe tool is determined to begin moving again. In practice, although itcan be difficult to determine the exact type of surgical timeout TO₂solely based on the video images, the nature of the detected event TO₂can be predicted or verified based on the recorded OR audio signalsduring the same time period as timeout event TO₂.

As yet another example of a non-surgical event taking place during agiven surgical phase, phase P₂ in surgical procedure 200 is shown toinclude a tool exchange event EX1 when one surgical tool is taken out ofthe patient's body and another tool is brought into the patient's body.The event corresponds to a time period designated as T_(EX1). Note that,in addition to the time needed to remove one tool and bring in anothertool, T_(EX1) can also include time that the surgeon has to wait for thenew tool to be brought to the OR and made ready for use. Because eventEX1 happens within phase P₂ when the endoscope video is available, theevent can be detected by analyzing the endoscope video, and thecorresponding duration T_(EX1) can be extracted. However, the detectionof event EX1 and the estimation of T_(EX1) can also be based on acollaborative analysis of the endoscope video and videos from theceiling and wall cameras.

Note that the above-described various non-surgical events within a givensurgical phase further break up that phase into a set of surgicalsegments, wherein the sum of the set of segments corresponds to theactual surgical procedure duration within the given phase, and the sumof all of the surgical segments from all of the phases corresponds tothe actual surgical procedure duration of the exemplary surgicalprocedure 200. However, instead of predicting for each of the surgicalsegments and calculating the sum, some embodiments detect and estimatethe two patient preparation times, various OOB times, variousnon-surgical times within each surgical phase, and then obtain theoverall non-surgical time as the sum of the above. The actual surgicalprocedure duration is then obtained by excluding the overallnon-surgical time from the duration of the overall surgical procedure200.

Referring still to FIG. 2, note that immediately after the last OOBevent T_(OOB4) is the post-surgery patient assistant (i.e., secondpatient preparation time) T_(prep2), which begins at t_(endo5) when theendoscope video stops and ends at t_(wo) when the patient is wheeled outof the OR. This is the final time period in the full surgical procedure200 after the final step of the surgical procedure is completed (thoughthe surgeon may or may not have left the OR). During this time periodthe surgical support team performs necessary steps to complete theoverall surgical procedure, such as closing up the incisions in thepatient, cleaning up the patient's body (e.g., removing an IV line),transferring the patient from the surgical platform to a wheeled bed,and finally wheeling the patient out of the OR. During the event ofT_(prep2), the endoscope video has ended. However, like the firstpreparation time T_(prep1), T_(prep2) can also be determined byanalyzing the videos from wall and/or ceiling cameras. In someembodiments, the second preparation time T_(prep2) can be determined bycombining the pressure sensor data from the surgical platform with theinformation from the wall/ceiling camera videos.

Eventually, when the patient is being wheeled out of the OR, the doorwaysensor can record the final moment t_(wo) of the full surgical procedure200. Hence, the overall duration P_(TOT) or the total OR time of thefull surgical procedure 200 can be expressed as:

P _(TOT) =t _(wo) −t _(wi).

Finally, the actual procedure duration P_(ACT) of the full surgicalprocedure 200 can be computed by excluding all of the above-describednon-surgical times from the overall duration P_(TOT)

P _(ACT) =P _(TOT)−(T _(prep1) +T _(prep2) +T _(OOB1) +T _(OOB2) T_(OOB3) +T _(OOB4) +T _(TO1) +T _(TO2) T _(EX1)).

Note that one of the applications of identifying OOB events during asurgical procedure is that the identified OOB events can be used tofacilitate HIPAA-compliant surgical video editing. During an OOB event,the endoscope camera can be pointed to various subjects to triggerprivacy violations, such as a whiteboard in the OR with patientinformation, a patient, or surgical support staff. Traditionally, toedit out such OOB segments from recorded surgical videos,HIPAA-compliance experts are hired to watch the videos, identify thoseOOB segments, and have the video segments blurred out. The disclosedtechnique to automatically identify OOB events can make HIPAA-compliantvideo editing a fully automated process.

FIG. 3 presents a flowchart illustrating an exemplary process 300 forperforming automatic HIPAA-compliant video editing in accordance withsome embodiments described herein. In one or more embodiments, one ormore of the steps in FIG. 3 may be omitted, repeated, and/or performedin a different order. Accordingly, the specific arrangement of stepsshown in FIG. 3 should not be construed as limiting the scope of thetechnique.

Process 300 begins by receiving an endoscope video captured by anendoscope camera during a surgical procedure (step 302). In someembodiments, the surgical procedure is a non-robotic minimally invasivesurgery (MIS) procedure. In some other embodiments, the surgicalprocedure is a robotic surgical procedure. Next, process 300 performs amachine-learning-based analysis on the endoscope video to identify oneor more OOB events (step 304). As mentioned above, an OOB event isdefined as a time period when the endoscope is taken out of thepatient's body while the endoscope camera continues to record. Hence,identifying an OOB event would require identifying two transitionalevents: (1) when the endoscope camera is being taken out of thepatient's body; and (2) when the endoscope camera is being placed backinto the patient's body. Note that each of these transitional events canbe detected based on a given pattern in a sequence of video images. Forexample, the beginning of the OOB event can be identified based on asequence of video images depicting when the endoscope camera is beingpulled from the patient's body to outside of the patient's body. Withinthe sequence of images, the identifiable pattern can include multipleframes initially showing an anatomy inside the body, followed bymultiple frames of dark images, which are further followed by videoframes showing various objects and/or personnel in the OR Similarly, theend of an OOB event can be identified based a sequence of images made upof multiple frames initially showing various objects and/or personnel inthe OR, followed by multiple frames of dark images, which are thenfollowed by multiple frames displaying an anatomy inside the body. Insome embodiments, a machine-learning model can be trained to detect afirst pattern indicating the beginning of an OOB event based on a firstsequence of video frames; and a second pattern indicating the end of anOOB event based on a second sequence of video frames. For each of theidentified OOB events, process 300 next performs a blurring operation toanonymize the video segment corresponding to the identified OOB event(step 306). Process 300 subsequently provides the edited endoscope videowith blurred-out OOB events for further post-procedural analysis (step308).

Note that the endoscope videos produced during the surgical procedureare often the best data source for post-procedural analysis, includingperforming machine-learning-based analysis. Using machine-learningand/or computer-vision analysis tools, an endoscopic video can be usedto detect and recognize surgical tools and determine when a surgicaltool is actually in contact with the tissue and whether that tool isactually moving. Based on such analyses, the endoscopic video can beused to determine various OOB events that have taken place during thesurgical procedure.

The endoscope videos can also be used to identify tool exchange events,i.e., based on performing computer-vision and/or machine-learning-basedtool detection and recognition analyses. The endoscope videos canadditionally be used to identify surgical timeouts, e.g., by identifyingstatic scenes within the videos indicating various timeout events, suchas OR room chats/discussions, waiting for collaborating surgeons, amongothers. More details of surgical tool detection based on surgical videoanalysis are described in a related patent application Ser. No.16/129,593, the contents of which are incorporated by reference herein.As described above, the endoscope videos can also be used to identify aswitching time between the robotic phase and the laparoscopic phase of ahybrid surgical procedure.

Furthermore, the endoscope video can be used to perform surgical phasesegmentation, i.e., segmenting a surgical procedure into a set ofpredefined phases, wherein each phase represents a particular stage ofthe surgical procedure that serves a unique and distinguishable purposein the entire surgical procedure. More details of the surgical phasesegmentation based on surgical video analysis are described in a relatedpatent application Ser. No. 15/987,782, the contents of which areincorporated by reference herein. Note that the set of phases P₁, P₂,and P₃ associated with surgical procedure 200, which are separated bythe set of OOB events, is not the same as the set of pre-defined phasesgenerated by the surgical phase segmentation procedure. In fact, it isnot necessary to perform the surgical phase segmentation in order toidentify the various non-surgical events. However, segmenting a givensurgical procedure into the set of pre-defined phases allows forassociating each identified non-surgical event with a given pre-definedphase. Because these non-surgical events generally mean delays in asurgical procedure, knowing which pre-defined phases include whichidentified non-surgical-related events can significantly improveunderstanding of a given surgical procedure. For example, identifyingwhich pre-defined surgical phase contains the highest number ofidentified delays can indicate a deficiency in the surgeon's skill.

In some embodiments, videos from wall and ceiling cameras capture eventstaking place inside the OR, such as personnel (both the surgeon and thesupport staff) movements. The captured personnel movements can be usedas a direct indicator and/or analyzed using image processing tools toidentify tool exchange events, OOB events, certain types of timeoutevents, and switching between robotic and laparoscopic phases.

In some embodiments, pressure sensor data from the tips or jaws of thesurgical tools can be used collaboratively with the endoscope video toreinforce the detection of some timeout events. For example, afterdetecting a timeout/static event based on the endoscope video, thestarting time and the ending time of the static event can be verifiedbased on the pressure sensor data. A person skilled in the art willappreciate that the pressure data from the tool tip sensor can indicatewhen the tool tip is actually in touch with the tissue and when the tooltip is not touching the tissue. The timestamps associated with thepressure data can then be used to validate the extracted timestamps forthe static event based on the endoscope video.

In some embodiments, the pressure sensor data from surgical tool tipscan also be used collaboratively with the endoscope video to detect anddetermine the time periods for some timeout events. For example, theendoscope video can be used as the first data source to detect a segmentof the video when the tool(s) in the FOV is not moving (e.g., incombination with observing the pulsing of the tissue). Next, thepressure sensor data from the tool tips around the time frames of thedetected timeout event can be used to pinpoint the beginning (e.g., whenthe pressure decreases to zero) and the end (e.g., when the pressureincreases from zero to a significant value) of the timeout event. Notethat using only one of the two data sources, i.e., either detecting fromthe endoscope video that the tool is not moving, or detecting from thepressure sensor that there is no pressure at the tool tip, may beinsufficient to determine whether the tool is completely static. Bycombining these two data sources, the output based on the collaborativeinformation can lead to much higher confidence levels in the accuracy ofthe detection.

In some embodiments, the pressure sensors on the patient's bed can beused to detect repositioning of the tools. For example, when the initialpositioning of the surgical tools and holes/ports in the body aredetermined to be improper to access certain anatomy, an additionalhole/port in the body may need to be created. When this happens, thecamera can usually remain in place, but one or more surgical tools mayneed to be repositioned. This repositioning may be categorized as a typeof surgical timeout because while the surgeon is trying to figure out aproper tool placement, no tool-tissue interaction is happening. Notethat if the repositioning requires repositioning of the patient on thesurgical platform, the pressure sensor on the platform can detect achange in pressure as a result. However, if the repositioning requireslittle change of the patient's position, it is more reliable to combinethe pressure sensor data with the videos from the wall and ceilingcameras to actually see the repositioning process.

FIG. 4 presents a block diagram illustrating the interrelationshipsamong various types of events 402-410 that constitute the total OR time400 of a surgical procedure and the set of data sources 412-422available inside the OR during the surgical procedure in accordance withsome embodiments described herein. As can be seen in FIG. 4, total ORtime 400 includes patient preparation times 402, actual procedureduration 404, OOB events 406, tool exchange events 408, and surgicaltimeout events 410. The set of data sources includes endoscope videos412, videos 414 from wall and ceiling cameras, pressure sensor data 416from the surgical platform, pressure sensor data 418 from the surgicaltools, pressure sensor data 420 from the OR doorway, and OR audio data422. Note that each arrow linking a given type of event and a given datasource represents that the given data source can be used, either on itsown or in conjunction with other data sources, to detect/identify thegiven type of event from the full surgical procedure.

As described above, total OR time 400 can be determined based onpressure sensor data 420 from the OR doorway, based on the videos 414captured by the wall/ceiling cameras, or based on a combination of thepressure sensor data 420 and videos 414. For example, if the total ORtime 400 determined based on pressure sensor data 420 is unreasonablyshort, videos 414 from the wall/ceiling cameras can be used to verify ormake corrections to the total OR time 400 determined solely based onpressure sensor data 420. Note that determining total OR time 400 is arelatively straightforward process and typically does not require usingcomputer-vision or machine-learning techniques.

As further described above, patient preparation times 402 can includethe pre-surgery preparation time T_(prep1) and the post-surgerypreparation time T_(prep2), and both can be directly determined based onvideos 414 from the wall and/or ceiling cameras. In some embodiments,these two patient preparation times 402 can also be determined based onthe pressure sensor data 416 from the surgical platform. For example, todetermine the first preparation time T_(prep1), pressure sensor data 416from the surgical platform can be analyzed to determine when the sensordata has increased from zero (without patient) to a significantly highervalue (when the patient is transferred onto the surgical platform), andhas stabilized (e.g., after positioning the patient on the surgicalplatform). Next, T_(prep1) can be determined as the time period betweenwheeled-in time t_(wi) and the moment when the pressure data on thesurgical platform stabilizes. Similarly, to determine the secondpreparation time T_(prep2), pressure sensor data 416 from the surgicalplatform can be analyzed to determine when the data has decreased from ahigher value (when the patient is still lying on the surgical platform)to a significantly lower value or zero (when the patient is removed fromthe surgical platform). Next, the T_(prep2) can be determined as thetime between the wheeled-out time t_(wo) and the time when the pressuredata on the surgical platform becomes zero. In some embodiments,T_(prep1) and T_(prep2) can be determined based on the combination ofthe pressure sensor data 416 from the surgical platform and videos 414captured by the wall/ceiling cameras.

As also described above, OOB events 406 can include OOB events duringthe actual surgical procedure (or “in-procedure OOB events”hereinafter), an initial OOB event at the beginning of the actualsurgical procedure, and a final OOB event at the end of the actualsurgical procedure. Moreover, each of the in-procedure OOB events can beidentified within the total OR time 400 based on analyzing endoscopevideos 412. In some embodiments, to identify a given in-procedure OOBevent from total OR time 400, a machine-learning model can be applied toendoscope videos 412 to identify the beginning of the OOB event based ona first sequence of video images, and to identify the end of the OOBevent based on a second sequence of video images. Note that prior toapplying the machine-learning model to identify OOB events from thetotal OR time 400, the model can be trained to classify a sequence ofvideo images as one of: (1) the beginning of an in-procedure OOB event;(2) the end of an in-procedure OOB event; and (3) neither of the above.

Also described earlier, if the endoscope camera is turned on prior tothe initial insertion of the endoscope into the patient's body, aninitial OOB event exists and needs to be detected. Note that to identifythe initial OOB event from the total OR time 400, only the end of theinitial OOB event needs to be detected. In some embodiments, the samemachine-learning model used for detecting in-procedure OOB events can beapplied to the beginning portion of the endoscope video to determinewhen the endoscope is initially inserted into the patient's body basedon a sequence of video images. Next, the initial OOB event can becalculated as the time between the beginning of an endoscope video 412and the determined initial insertion time.

Similarly, if the endoscope camera remains turned on for some time afterthe final removal of the endoscope from the patient's body, a final OOBevent exists and needs to be detected. Note that to identify the finalOOB event from the total OR time 400, only the beginning of the finalOOB event needs to be detected. In some embodiments, the samemachine-learning model used for detecting in-procedure OOB events can beapplied to the final portion of an endoscope video 412 to determine whenthe endoscope is finally removed from the patient's body based on asequence of video images. Next, the final OOB event can be calculated asthe time between the end of the endoscope video 412 and the determinedfinal endoscope removal time.

Note that all of the above OOB events can also be directly identifiedbased on videos 414 from the wall/ceiling cameras. For example, for anin-procedure OOB event related to lens cleaning, one or more videos 414captured in the OR during the surgical procedure may capture the eventincluding the moment when the endoscope is taken out of the patient'sbody and the moment when the endoscope is re-inserted into the patient'sbody after lens cleaning and camera recalibration. In some embodiments,an in-procedure OOB event can be determined based on the combination ofendoscope videos 412 and videos 414 from the wall/ceiling cameras. Forexample, if analyzing endoscope videos 412 fails to identify either thebeginning or the end of an in-procedure OOB event, the correspondingvideos 414 can be used to help identify the missing timestamp. Morespecifically, if analyzing endoscope videos 412 has identified thebeginning of an OOB event t_(b), but fails to identify the end of theOOB event t_(e), videos 414 from the wall and ceiling cameras can bereviewed from the time t_(b) onward till the point when the endoscope isbeing re-inserted into the camera port. Similarly, if analyzingendoscope videos 412 has identified the end of the OOB event t_(e), butfails to identify the beginning of the OOB event t_(b), videos 414 fromthe wall and ceiling cameras can be reviewed from the time t_(e)backward till the point when the endoscope is being removed from thecamera port.

Tool exchange events 408 make up another portion of total OR time 400that does not belong to actual procedure duration 404. As describedabove, each of the tool exchange events 408 can be determined based onanalyzing endoscope videos 412. Because detecting a tool exchange eventinvolves detecting and recognizing multiple surgical tools, amachine-learning-based analysis can be applied to endoscope videos 412,e.g., by individually identifying the beginning and the end of a toolexchange event. For example, a machine-learning-based video analysistool may continue to detect and recognize a first surgical tool (e.g., asurgical stapler) up until image frame i, but begins to detect andrecognize a second surgical tool (e.g., a surgical grasper) startingfrom image frame i+50. In between image frames i and i+50, no surgicaltool is detected from endoscope videos 412. The video analysis tool canthen conclude that a tool exchange event 408 has been detected startingfrom frame i and ending at frame i+50, and a corresponding tool exchangetime can also be extracted.

In some embodiments, a tool exchange event 408 can also be detectedbased on pressure sensor data 418 from tips or jaws of the two surgicaltools involved in the tool exchange event. For example, the beginning ofthe tool exchange event 408 can be determined as the time when thepressure sensor data 418 from the first surgical tool (which is beingreplaced) goes from a finite value to zero, whereas the end of the toolexchange event 408 can be determined as the time when the pressuresensor data 418 from the second surgical tool (which is replacing thefirst surgical tool) goes from zero to a finite value. In someembodiments, a tool exchange event 408 can be identified based on thecombination of endoscope videos 412 and pressure sensor data 418 fromthe surgical tools involved in the tool exchange event. In someembodiments, a tool exchange event 408 can also be identified based onthe combination of endoscope videos 412, pressure sensor data 418 fromthe surgical tools involved in the tool exchange event, and videos 414from the wall and ceiling cameras.

Surgical timeout events 410, such as OR room chats/discussions orwaiting for collaborating surgeons, make up for another portion of thetotal OR time 400 that does not belong to actual procedure duration 404.As described above, during a surgical timeout, the surgeon is not doinganything with a surgical tool in the FOV of an endoscope video;therefore, the surgical tool is idle and the scene appears static in theendoscope video. Hence, each of the surgical timeout events 408 can beidentified based on analyzing the corresponding video 412. For example,by analyzing a sequence of images and recognizing that the scene has notchanged over a predetermined number of frames (e.g., 20 frames), atimeout event can be identified. Note that often-times the scene of theendoscope video is not completely static due to pulsing of the organs inthe FOV. However, the pulsing can be extracted as the background noise,or used as a signature indicating a static scene (when pulsing is theonly action).

In some embodiments, a surgical timeout event 410 can also be detectedbased on pressure sensor data 418 from the surgical tools involved inthe event. For example, the beginning of a surgical timeout event 410can be identified as the time when pressure sensor data 418 from thesurgical tool goes from a finite value to zero (i.e., becomes inactive),whereas the end of the surgical timeout event 410 can be identified asthe time when pressure sensor data 418 from the surgical tool goes fromzero to a finite value (i.e., becomes active again). In someembodiments, a surgical timeout event 410 can be determined based on thecombination of the endoscope video 412 and pressure sensor data 418 fromthe surgical tools involved in the event. In some embodiments, asurgical timeout event 410 can also be determined based on videos 414from the wall and ceiling cameras.

Note that audio data 422 recorded during total OR time 400 can includeverbal exchanges between the surgeon and other people both inside andoutside the OR. At any point in time during total OR time 400, audiodata 422 provide clues to the ongoing event. Hence, audio data 422 canbe used collaboratively with any of the other data sources 412-420 todetect each type of non-surgical event 406-410, or to improve theaccuracy and confidence of the detection of such events initially basedon data sources 412-420. In some embodiments, processing audio data 422in collaboration with data sources 412-420 for non-surgical eventdetection can include performing natural language processing on audiodata 422.

As can be seen, the disclosed surgical procedure analysis system allowsfor identifying and extracting the corresponding duration for eachnon-surgical event and each type of the described non-surgical events.Once all of the above information is available, the full surgicalprocedure and the corresponding total OR time duration can be broken upinto the different segments described above. The extracted timeinformation of various surgical and non-surgical events can be used toperform in-depth analysis of and provide insight into the correspondingsurgical procedure, e.g., by evaluating individually or in combination(e.g., as a combined time of the same type of non-surgical event) todetermine its impact on the total OR time (e.g., in term of percentageof total OR time 400). For example, the total recorded tool exchangetime can be used to evaluate the impact of tool exchange events on thetotal OR time. Note that tool recognition information extracted alongwith detecting tool exchange events can also be used for the purposes oftool inventory, and tracking how many times surgical tools are used orfired. The extracted time information of various surgical andnon-surgical events is also correlated to, and therefore can also beused to perform assessments of, the following metrics:

-   -   OR workflow efficiency;    -   OR cost;    -   Effect on anesthesia dosage;    -   Effect on infection rates;    -   Surgeon skill evaluations; and    -   Surgical procedure outcomes analysis.        The results from the above evaluations can then guide the        relevant hospitals, surgeons, and surgical support teams to make        corrections and adjustments to their OR practices.

Moreover, the time information for a given type of non-surgical eventcan be used for item-to-item comparisons between different surgicalprocedures and different surgeons. As a concrete example, for twosurgical procedures A and B of the same type performed by two surgeons Aand B, the total OR times indicate that surgeon A spends 20 minuteslonger in the OR than surgeon B. After extracting various non-surgicalevents, it is determined that the actual procedure durations of the twoprocedures are substantially the same. However, procedure A has 20minutes more in determined overall timeout events than procedure B,e.g., due to the discussions of surgeon A with others in the OR. Thisanalysis can reveal that surgeon A is less efficient as a result oftaking too many timeouts.

FIG. 5 shows a block diagram of an exemplary surgical procedure analysissystem 500 in accordance with some embodiments described herein. As canbe seen in FIG. 5, surgical procedure analysis system 500 includes ORdata sources 502 that further include endoscope videos 504, wall/ceilingcamera videos 506, pressure sensor data 508, and OR audio data 510, eachof which is collected inside an OR during a surgical procedure. Notethat pressure sensor data 508 can further include pressure sensor datafrom surgical tool tips or jaws, pressure sensor data from a surgicalplatform, and pressure sensor data from an OR doorway.

As can be seen in FIG. 5, surgical procedure analysis system 500 furtherincludes a surgical procedure analysis subsystem 512, which is coupledto OR data sources 502 and configured to receive various video, audioand sensor data 504-510 from OR data sources 502 and perform variousabove-described surgical procedure analyses to break down a total ORtime by identifying various disclosed surgical and non-surgicalsegments/events and extract the duration of each of the identifiedsurgical or non-surgical segments/events. More specifically, surgicalprocedure analysis subsystem 512 can include a set of machine-learningmodules that can be applied to one or more of the data sources 504-510to identify various surgical and non-surgical events.

For example, these machine-learning modules can include amachine-learning module 514 for identifying a set of OOB events duringthe total OR time mainly based on endoscope videos 504; amachine-learning module 516 for identifying a set of tool exchangeevents during the total OR time mainly based on endoscope videos 504; amachine-learning module 518 for identifying a set of surgical timeoutevents during the total OR time mainly based on endoscope videos 504;and a machine-learning module 520 for identifying a set of actualsurgical segments during the total OR time mainly based on endoscopevideos 504. These machine-learning modules can also include anatural-language-processing module 522 for analyzing OR audio data 510,and the output from natural-language-processing module 522 can be usedby machine-learning modules 514-520 to improve the accuracy of detectionof various surgical and non-surgical events. Note that surgicalprocedure analysis subsystem 512 also includes anactual-procedure-extraction module 524 that can use the output frommachine-learning modules 514-520 to extract the actual procedureduration from the total OR time.

FIG. 6 conceptually illustrates a computer system with which someembodiments of the subject technology can be implemented. Computersystem 600 can be a client, a server, a computer, a smartphone, a PDA, alaptop, or a tablet computer with one or more processors embeddedtherein or coupled thereto, or any other sort of computing device. Sucha computer system includes various types of computer-readable media andinterfaces for various other types of computer-readable media. Computersystem 600 includes a bus 602, processing unit(s) 612, a system memory604, a read-only memory (ROM) 610, a permanent storage device 608, aninput device interface 614, an output device interface 606, and anetwork interface 616. In some embodiments, computer system 600 is apart of a robotic surgical system.

Bus 602 collectively represents all system, peripheral, and chipsetbuses that communicatively connect the numerous internal devices ofcomputer system 600. For instance, bus 602 communicatively connectsprocessing unit(s) 612 with ROM 610, system memory 604, and permanentstorage device 608.

From these various memory units, processing unit(s) 612 retrievesinstructions to execute and data to process in order to execute variousprocesses described in this patent disclosure, including theabove-described surgical procedure analyses to detect/identify variousdisclosed surgical and non-surgical events, extract the durations of theidentified events, and extract the actual procedure duration from thetotal OR time described in conjunction with FIGS. 1-5. The processingunit(s) 612 can include any type of processor, including but not limitedto, a microprocessor, a graphic processing unit (GPU), a tensorprocessing unit (TPU), an intelligent processor unit (IPU), a digitalsignal processor (DSP), a field-programmable gate array (FPGA), and anapplication-specific integrated circuit (ASIC). Processing unit(s) 612can be a single processor or a multi-core processor in differentimplementations.

ROM 610 stores static data and instructions that are needed byprocessing unit(s) 612 and other modules of the computer system.Permanent storage device 608, on the other hand, is a read-and-writememory device. This device is a non-volatile memory unit that storesinstructions and data even when computer system 600 is off. Someimplementations of the subject disclosure use a mass-storage device(such as a magnetic or optical disk and its corresponding disk drive) aspermanent storage device 608.

Other implementations use a removable storage device (such as a floppydisk, flash drive, and its corresponding disk drive) as permanentstorage device 608. Like permanent storage device 608, system memory 604is a read-and-write memory device. However, unlike storage device 608,system memory 604 is a volatile read-and-write memory, such as a randomaccess memory. System memory 604 stores some of the instructions anddata that the processor needs at runtime. In some implementations,various processes described in this patent disclosure, including theprocesses of detecting/identifying various disclosed surgical andnon-surgical events, extracting the durations of the identified events,and extracting the actual procedure duration from the total OR timedescribed in conjunction with FIGS. 1-5, are stored in system memory604, permanent storage device 608, and/or ROM 610. From these variousmemory units, processing unit(s) 612 retrieves instructions to executeand data to process in order to execute the processes of someimplementations.

Bus 602 also connects to input and output device interfaces 614 and 606.Input device interface 614 enables the user to communicate informationto and select commands for the computer system. Input devices used withinput device interface 614 include, for example, alphanumeric keyboardsand pointing devices (also called “cursor control devices”). Outputdevice interface 606 enables, for example, the display of imagesgenerated by computer system 600. Output devices used with output deviceinterface 606 include, for example, printers and display devices, suchas cathode ray tubes (CRT) or liquid crystal displays (LCD). Someimplementations include devices such as a touchscreen that functions asboth input and output devices.

Finally, as shown in FIG. 6, bus 602 also couples computer system 600 toa network (not shown) through a network interface 616. In this manner,the computer can be a part of a network of computers (such as a localarea network (“LAN”), a wide area network (“WAN”), an intranet, or anetwork of networks, such as the Internet. Any or all components ofcomputer system 600 can be used in conjunction with the subjectdisclosure.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedin this patent disclosure may be implemented as electronic hardware,computer software, or combinations of both. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure.

The hardware used to implement the various illustrative logics, logicalblocks, modules, and circuits described in connection with the aspectsdisclosed herein may be implemented or performed with a general purposeprocessor, a digital signal processor (DSP), an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A general-purpose processor maybe a microprocessor, but in the alternative, the processor may be anyconventional processor, controller, microcontroller, or state machine. Aprocessor may also be implemented as a combination of receiver devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Alternatively, some steps ormethods may be performed by circuitry that is specific to a givenfunction.

In one or more exemplary aspects, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable storagemedium or non-transitory processor-readable storage medium. The steps ofa method or algorithm disclosed herein may be embodied inprocessor-executable instructions that may reside on a non-transitorycomputer-readable or processor-readable storage medium.

Non-transitory computer-readable or processor-readable storage media maybe any storage media that may be accessed by a computer or a processor.By way of example but not limitation, such non-transitorycomputer-readable or processor-readable storage media may include RAM,ROM, EEPROM, flash memory, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othermedium that may be used to store desired program code in the form ofinstructions or data structures and that may be accessed by a computer.The terms “disk” and “disc,” as used herein, include compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk, andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveare also included within the scope of non-transitory computer-readableand processor-readable media. Additionally, the operations of a methodor algorithm may reside as one or any combination or set of codes and/orinstructions on a non-transitory processor-readable storage mediumand/or computer-readable storage medium, which may be incorporated intoa computer-program product.

While this patent document contains many specifics, these should not beconstrued as limitations on the scope of any disclosed technology or ofwhat may be claimed, but rather as descriptions of features that may bespecific to particular embodiments of particular techniques. Certainfeatures that are described in this patent document in the context ofseparate embodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can, in some cases, be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in this patent document should not be understoodas requiring such separation in all embodiments.

Only a few implementations and examples are described, and otherimplementations, enhancements and variations can be made based on whatis described and illustrated in this patent document.

What is claimed is:
 1. A computer-implemented method for extracting anactual procedure duration composed of actual surgical tool-tissueinteractions from an overall procedure duration of a surgical procedureon a patient, the method comprising: obtaining an overall procedureduration of the surgical procedure performed by a surgeon on thepatient; receiving a set of operating room (OR) data from a set of ORdata sources collected during the surgical procedure, wherein the set ofOR data includes an endoscope video captured during the surgicalprocedure; analyzing the set of OR data to detect a set of non-surgicalevents during the surgical procedure that do not involve surgicaltool-tissue interactions, wherein analyzing the set of OR data includesperforming a machine-learning-based analysis on the endoscope video;extracting a set of durations corresponding to the set of non-surgicalevents; and determining the actual procedure duration by subtracting theset of durations corresponding to the set of non-surgical events fromthe overall procedure duration.
 2. The computer-implemented method ofclaim 1, wherein the set of OR data further includes one or more of thefollowing: a set of sensor data collected inside the OR during thesurgical procedure; a set of audio files recorded inside the OR duringthe surgical procedure; and one or more videos captured by one or morewall and/or ceiling cameras inside the OR during the surgical procedure.3. The computer-implemented method of claim 2, wherein the set of sensordata further includes one or more of the following: pressure sensor datacollected from surgical tools involved in the surgical procedure;pressure sensor data collected from a surgical platform inside the OR;and pressure sensor data collected from a doorway of the OR.
 4. Thecomputer-implemented method of claim 1, wherein analyzing the set of ORdata to detect a set of non-surgical events further includes identifyinga surgical timeout event, wherein a surgical timeout event occurs withina surgical phase of the surgical procedure when the surgeon pausesperforming surgical tasks on the patient for a certain time period. 5.The computer-implemented method of claim 4, wherein identifying thesurgical timeout event includes performing a machine-learning-basedanalysis on the endoscope video to determine that the movement of asurgical tool in the endoscope video has stopped for more than apredetermined time period.
 6. The computer-implemented method of claim5, wherein extracting the duration of the identified surgical timeoutevent includes: extracting an initial time of the identified surgicaltimeout event when the movement of the surgical tool is determined tohave stopped based on the machine-learning-based analysis; andextracting an end time of the identified surgical timeout event when themovement the surgical tool is determined to have resumed based on themachine-learning-based analysis.
 7. The computer-implemented method ofclaim 6, wherein the method further comprises collaborating theextracted initial time and end time of the identified surgical timeoutevent with pressure sensor data collected from a pressure sensor locatedat the tip of the surgical tool.
 8. The computer-implemented method ofclaim 7, wherein collaborating the extracted initial time and end timewith the pressure sensor data includes: collaborating the extractedinitial time with a first time when the pressure sensor data decreasesto substantially zero; and collaborating the extracted end time with asecond time when the pressure sensor data increases from substantiallyzero to a significant value.
 9. The computer-implemented method of claim4, wherein the surgical timeout event occurs for one of the followingset of reasons: when the surgeon stops interacting with the patient andstarts a discussion with another surgeon, the surgical support team, ora resident surgeon; when the surgeon pauses to make a decision on how toproceed with the surgical procedure based on a on-screen event or asurgical complication; and when the surgeon pauses to wait for acollaborating surgeon to come into the OR.
 10. The computer-implementedmethod of claim 1, wherein analyzing the set of OR data to detect a setof non-surgical events during the surgical procedure further includesidentifying a set of out-of-body (OOB) events, wherein an OOB eventbegins when an endoscope used during the surgical procedure is taken outof the patient's body for one of a set of reasons and ends when theendoscope is being inserted back into the patient's body.
 11. Thecomputer-implemented method of claim 10, wherein identifying an OOBevent includes: performing a machine-learning-based analysis on theendoscope video to: identify the beginning of the OOB event based on afirst sequence of video images in the endoscope video; and identify theend of the OOB event based on a second sequence of video images in theendoscope video.
 12. The computer-implemented method of claim 10,wherein a given OOB event occurs because of one of a following set ofreasons: cleaning the endoscope lens when an endoscopic view ispartially or entirely blocked; changing the endoscope lens from onescope size to another scope size; and switching the surgical procedurefrom a robotic surgical system to a laparoscopic surgical system. 13.The computer-implemented method of claim 1, wherein analyzing the set ofOR data to detect a set of non-surgical events during the surgicalprocedure further includes: identifying a pre-surgery patientpreparation time prior to the surgical procedure; and identifying apost-surgery patient assistant time after the completion of the surgicalprocedure.
 14. The computer-implemented method of claim 1, whereinobtaining the overall procedure duration of the surgical procedureincludes determining a time when the patient is being wheeled into theOR and a time when the patient is being wheeled out of the OR.
 15. Asystem for extracting an actual procedure duration composed of actualsurgical tool-tissue interactions from an overall procedure duration ofa surgical procedure on a patient, the system comprising: one or moreprocessors; and a memory coupled to the one or more processors, whereinthe memory stores instructions that, when executed by the one or moreprocessors, cause the system to: obtain an overall procedure duration ofthe surgical procedure performed by a surgeon on the patient; receive aset of operating room (OR) data from a set of OR data sources collectedduring the surgical procedure, wherein the set of OR data includes anendoscope video captured during the surgical procedure; analyze the setof OR data to detect a set of non-surgical events during the surgicalprocedure that do not involve surgical tool-tissue interactions, whereinanalyzing the set of OR data includes performing amachine-learning-based analysis on the endoscope video; extract a set ofdurations corresponding to the set of non-surgical events; and determinethe actual procedure duration by subtracting the set of durationscorresponding to the set of non-surgical events from the overallprocedure duration.
 16. The system of claim 15, wherein the memoryfurther stores instructions that, when executed by the one or moreprocessors, cause the system to analyze the set of OR data to detect aset of non-surgical events by: identifying a surgical timeout event,wherein a surgical timeout event occurs within a surgical phase of thesurgical procedure when the surgeon pauses performing surgical tasks onthe patient for a certain time period, wherein identifying the surgicaltimeout event includes performing a machine-learning-based analysis onthe endoscope video to determine that the movement of a surgical tool inthe endoscope video has stopped for more than a predetermined timeperiod.
 17. The system of claim 16, wherein the memory further storesinstructions that, when executed by the one or more processors, causethe system to extract the duration of the identified surgical timeoutevent by: extracting an initial time of the identified surgical timeoutevent when the movement of the surgical tool is determined to havestopped based on the machine-learning-based analysis; and extracting anend time of the identified surgical timeout event when the movement thesurgical tool is determined to have resumed based on themachine-learning-based analysis.
 18. A computer-implemented method forextracting an actual procedure duration composed of actual surgicaltool-tissue interactions from an overall procedure duration of asurgical procedure on a patient, the method comprising: obtaining anoverall procedure duration of the surgical procedure performed by asurgeon on the patient; receiving a set of operating room (OR) data froma set of OR data sources collected during the surgical procedure,wherein the set of OR data includes an endoscope video captured duringthe surgical procedure and a set of sensor data collected during thesurgical procedure; analyzing the set of OR data to detect a set ofnon-surgical events during the surgical procedure that do not involvesurgical tool-tissue interactions, wherein analyzing the set of OR dataincludes collaborating the endoscope video with the set of sensor data;extracting a set of durations corresponding to the set of non-surgicalevents; and determining the actual procedure duration by subtracting theset of durations corresponding to the set of non-surgical events fromthe overall procedure duration.
 19. The computer-implemented method ofclaim 18, wherein the set of sensor data further includes one or more ofthe following: pressure sensor data collected from surgical toolsinvolved in the surgical procedure; pressure sensor data collected froma surgical platform inside the OR; and pressure sensor data collectedfrom a doorway of the OR.
 20. The computer-implemented method of claim18, wherein the set of OR data further includes: a set of audio filesrecorded inside the OR during the surgical procedure; and one or morevideos captured by one or more wall and/or ceiling cameras inside the ORduring the surgical procedure.
 21. A computer-implemented method forextracting an actual procedure duration composed of actual surgicaltool-tissue interactions from an overall procedure duration of asurgical procedure on a patient, the method comprising: obtaining anoverall procedure duration of the surgical procedure performed by asurgeon on the patient; receiving a set of operating room (OR) data froma set of OR data sources collected during the surgical procedure,wherein the set of OR data includes an endoscope video captured duringthe surgical procedure; analyzing the set of OR data to detect a set ofnon-surgical events during the surgical procedure that do not involvesurgical tool-tissue interactions, wherein analyzing the set of OR datato detect a set of non-surgical events includes identifying a set ofout-of-body (00B) events, and wherein an OOB event begins when anendoscope used during the surgical procedure is taken out of thepatient's body for one of a set of reasons and ends when the endoscopeis being inserted back into the patient's body; extracting a set ofdurations corresponding to the set of non-surgical events; anddetermining the actual procedure duration by subtracting the set ofdurations corresponding to the set of non-surgical events from theoverall procedure duration.
 22. The computer-implemented method of claim21, wherein identifying an OOB event in the set of OOB events includes:performing a machine-learning-based analysis on the endoscope video to:identify the beginning of the OOB event based on a first sequence ofvideo images in the endoscope video; and identify the end of the OOBevent based on a second sequence of video images in the endoscope video.23. The computer-implemented method of claim 21, wherein a given OOBevent in the set of OOB events occurs because of one of a following setof reasons: cleaning the endoscope lens when an endoscopic view ispartially or entirely blocked; changing the endoscope lens from onescope size to another scope size; and switching the surgical procedurefrom a robotic surgical system to a laparoscopic surgical system.